Method for Determining Appropriate Clustering Criteria of Location-Sensing Data

Youngmin Lee,Pil Kwon,Woojin Park,Kiyun Yu

doi:10.3390/ijgi5090151

Abstract

Large quantities of location-sensing data are generated from location-based social network services. These data are provided as point properties with location coordinates acquired from a global positioning system or Wi-Fi signal. To show the point data on multi-scale map services, the data should be represented by clusters following a grid-based clustering method, in which an appropriate grid size should be determined. Currently, there are no criteria for determining the proper grid size, and the modifiable areal unit problem has been formulated for the purpose of addressing this issue. The method proposed in this paper is applies a hexagonal grid to geotagged Twitter point data, considering the grid size in terms of both quantity and quality to minimize the limitations associated with the modifiable areal unit problem. Quantitatively, we reduced the original Twitter point data by an appropriate amount using Töpfer’s radical law. Qualitatively, we maintained the original distribution characteristics using Moran’s I. Finally, we determined the appropriate sizes of clusters from zoom levels 9–13 by analyzing the distribution of data on the graphs. Based on the visualized clustering results, we confirm that the original distribution pattern is effectively maintained using the proposed method.

Highlights

Map generalization refers to the process of expressing point features as clusters by merging points on a multi-scale map
The study presented in this paper focuses on the modifiable areal unit problem (MAUP) scale effect, whereby we analyze the distribution characteristics resulting from changes in the grid sizes
We considered the hexagonal grids generated for each zoom level in Seoul as the unit of analysis

Summary

Introduction

Map generalization refers to the process of expressing point features as clusters by merging points on a multi-scale map. Map generalization is typically considered in terms of both quantity and quality. We consider the point clustering process from the perspective of a map point feature-generalization process. In the map generalization field, studies on line generalization [2,3,4,5,6,7] or polygon generalization [8,9,10,11] have been conducted for some time. Studies on point generalization have been minimally performed [1], because most web map components are linear features, such as roads and streams, or polygonal features, such as buildings and parcels. On the other hand, tend to be to be considered of minimal importance

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